Can classical surface plasmon resonance advance via the coupling to other analytical approaches?
Bibliographic record
Abstract
For nearly 40 years, surface plasmon resonance (SPR) analysis has been used to better understand the binding interaction strength between surface immobilized bioreceptors and the analytes of interest. The advantage of surface plasmon resonance, over other affinity sensing approaches such as Western blots and ELISAs approaches, resides in its possibility to reveal binding kinetics in a label-free manner. The concept of surface plasmon resonance has in addition been widely employed for the development of biosensors capitalizing on its direct assay format, short response times, simple sample treatments along with multiplexed sensing possibilities. To this must be added the possibility to reach high sensitivity due to the capability of surface plasmon resonance to detect very small changes in refractive index at the sensing interfaces in particular for analytes of larger size such as cells (e.g., bacteria), proteins, peptides and oligonucleotides. Challenges inherent to all affinity approaches call for further research and include non-specific surface binding events, mass transportation restrictions, steric hindrance, and the risk of data misinterpretation in case of lack of selective analyte binding. This opinion article is devoted to outlining the different approaches proposed to address these challenges by e.g., coupling with fluorescence read out, electrochemical sensing, mass spectroscopy analysis and more recently to integrate lateral flow concepts into surface plasmon resonance. Other plasmonic methods such as localized surface plasmon resonance (LSPR), surface enhanced Raman spectroscopy (SERS) will not be considered in detail, as such techniques have nowadays their own standing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".